Introduction to Pie Charts in Plotly

contrib/plotting-visualization/index.md

@@ -10,3 +10,4 @@
 - [Seaborn Plotting Functions](seaborn-plotting.md)
 - [Getting started with Seaborn](seaborn-basics.md)
 - [Bar Plots in Plotly](plotly-bar-plots.md)
+- [Pie Charts in Plotly](plotly-pie-charts.md)

contrib/plotting-visualization/plotly-pie-charts.md

@@ -0,0 +1,221 @@
+# Pie Charts in Plotly
+
+A pie chart is a type of graph that represents the data in the circular graph. The slices of pie show the relative size of the data, and it is a type of pictorial representation of data. A pie chart requires a list of categorical variables and numerical variables. Here, the term "pie" represents the whole, and the "slices" represent the parts of the whole.
+
+Pie charts are commonly used in business presentations like sales, operations, survey results, resources, etc. as they are pleasing to the eye and provide a quick summary.
+
+Plotly is a very powerful library for creating modern visualizations and it provides a very easy and intuitive method to create highly customized pie charts.
+
+## Prerequisites
+
+Before creating bar plots in Plotly you must ensure that you have Python, Plotly and Pandas installed on your system.
+
+## Introduction
+
+There are various ways to create pie charts in `plotly`. One of the prominent and easiest one is using `plotly.express`. Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on a variety of types of data and produces easy-to-style figures. On the other hand you can also use `plotly.graph_objects` to create various plots.
+
+Here, we'll be using `plotly.express` to create the pie charts. Also we'll be converting our datasets into pandas DataFrames which makes it extremely convenient and easy to create charts.
+
+Also, note that when you execute the codes in a simple python file, the output plot will be shown in your **browser**, rather than a pop-up window like in matplotlib. If you do not want that, it is **recommended to create the plots in a notebook (like jupyter)**. For this, install an additional library `nbformat`. This way you can see the output on the notebook itself, and can also render its format to png, jpg, etc.
+
+## Creating a simple pie chart using `plotly.express.pie`
+
+In `plotly.express.pie`, data visualized by the sectors of the pie is set in values. The sector labels are set in names.
+
+```Python
+import plotly.express as px
+import pandas as pd
+
+# Creating dataset
+flowers = ['Rose','Tulip','Marigold','Sunflower','Daffodil']
+petals = [11,9,17,4,7]
+
+# Converting dataset to pandas DataFrame
+dataset = {'flowers':flowers, 'petals':petals}
+df = pd.DataFrame(dataset)
+
+# Creating pie chart
+fig = px.pie(df, values='petals', names='flowers')
+
+# Showing plot
+fig.show()
+```
+![Basic Pie Chart](images/plotly-basic-pie-chart.png)
+
+Here, we are first creating the dataset and converting it into Pandas DataFrames using dictionaries, with its keys being DataFrame columns. Next, we are plotting the pie chart by using `px.pie`. In the `values` and `names` parameters, we have to specify a column name in the DataFrame.
+
+`px.pie(df, values='Petals', names='Flowers')` is used to specify that the pie chart is to be plotted by taking the values from column `Petals` and the fractional area of each slice is represented by **petal/sum(petals)**. The column `flowers` represents the labels of slices corresponding to each value in `petals`.
+
+**Note:** When you generate the image using above code, it will show you an **interactive plot**, if you want image, you can download it from their itself.
+
+## Customizing Pie Charts
+
+### Adding title to the chart
+
+Simply pass the title of your chart as a parameter in `px.pie`.
+
+```Python
+import plotly.express as px
+import pandas as pd
+
+# Creating dataset
+flowers = ['Rose','Tulip','Marigold','Sunflower','Daffodil']
+petals = [11,9,17,4,7]
+
+# Converting dataset to pandas DataFrame
+dataset = {'flowers':flowers, 'petals':petals}
+df = pd.DataFrame(dataset)
+
+# Creating pie chart
+fig = px.pie(df, values='petals', names='flowers',
+             title='Number of Petals in Flowers')
+
+# Showing plot
+fig.show()
+```
+![Title in Pie Chart](images/plotly-pie-title.png)
+
+### Coloring Slices
+
+There are a lot of beautiful color scales available in plotly and can be found here [plotly color scales](https://plotly.com/python/builtin-colorscales/). Choose your favourite colorscale apply it like this:
+
+```Python
+import plotly.express as px
+import pandas as pd
+
+# Creating dataset
+flowers = ['Rose','Tulip','Marigold','Sunflower','Daffodil']
+petals = [11,9,17,4,7]
+
+# Converting dataset to pandas DataFrame
+dataset = {'flowers':flowers, 'petals':petals}
+df = pd.DataFrame(dataset)
+
+# Creating pie chart
+fig = px.pie(df, values='petals', names='flowers',
+             title='Number of Petals in Flowers',
+             color_discrete_sequence=px.colors.sequential.Agsunset)
+
+# Showing plot
+fig.show()
+```
+![Pie Chart Colors-1](images/plotly-pie-color-1.png)
+
+You can also set custom colors for each label by passing it as a dictionary(map) in `color_discrete_map`, like this:
+
+```Python
+import plotly.express as px
+import pandas as pd
+
+# Creating dataset
+flowers = ['Rose','Tulip','Marigold','Sunflower','Daffodil']
+petals = [11,9,17,4,7]
+
+# Converting dataset to pandas DataFrame
+dataset = {'flowers':flowers, 'petals':petals}
+df = pd.DataFrame(dataset)
+
+# Creating pie chart
+fig = px.pie(df, values='petals', names='flowers',
+             title='Number of Petals in Flowers',
+             color='flowers',
+             color_discrete_map={'Rose':'red',
+                                 'Tulip':'magenta',
+                                 'Marigold':'green',
+                                 'Sunflower':'yellow',
+                                 'Daffodil':'royalblue'})
+
+# Showing plot
+fig.show()
+```
+![Pie Chart Colors-1](images/plotly-pie-color-2.png)
+
+### Labeling Slices
+
+You can use `fig.update_traces` to effectively control the properties of text being displayed on your figure, for example if we want both flower name , petal count and percentage in our slices, we can do it like this:
+
+```Python
+import plotly.express as px
+import pandas as pd
+
+# Creating dataset
+flowers = ['Rose','Tulip','Marigold','Sunflower','Daffodil']
+petals = [11,9,17,4,7]
+
+# Converting dataset to pandas DataFrame
+dataset = {'flowers':flowers, 'petals':petals}
+df = pd.DataFrame(dataset)
+
+# Creating pie chart
+fig = px.pie(df, values='petals', names='flowers',
+             title='Number of Petals in Flowers')
+
+# Updating text properties
+fig.update_traces(textposition='inside', textinfo='label+value+percent')
+
+# Showing plot
+fig.show()
+```
+![Pie Labels](images/plotly-pie-labels.png)
+
+### Pulling out a slice
+
+To pull out a slice pass an array to parameter `pull` in `fig.update_traces` corresponding to the slices and amount to be pulled.
+
+```Python
+import plotly.express as px
+import pandas as pd
+
+# Creating dataset
+flowers = ['Rose','Tulip','Marigold','Sunflower','Daffodil']
+petals = [11,9,17,4,7]
+
+# Converting dataset to pandas DataFrame
+dataset = {'flowers':flowers, 'petals':petals}
+df = pd.DataFrame(dataset)
+
+# Creating pie chart
+fig = px.pie(df, values='petals', names='flowers',
+             title='Number of Petals in Flowers')
+
+# Updating text properties
+fig.update_traces(textposition='inside', textinfo='label+value')
+
+# Pulling out slice
+fig.update_traces(pull=[0,0,0,0.2,0])
+
+# Showing plot
+fig.show()
+```
+![Slice Pull](images/plotly-pie-pull.png)
+
+### Pattern Fills
+
+You can also add patterns (hatches), in addition to colors in pie charts.
+
+```Python
+import plotly.express as px
+import pandas as pd
+
+# Creating dataset
+flowers = ['Rose','Tulip','Marigold','Sunflower','Daffodil']
+petals = [11,9,17,4,7]
+
+# Converting dataset to pandas DataFrame
+dataset = {'flowers':flowers, 'petals':petals}
+df = pd.DataFrame(dataset)
+
+# Creating pie chart
+fig = px.pie(df, values='petals', names='flowers',
+             title='Number of Petals in Flowers')
+
+# Updating text properties
+fig.update_traces(textposition='outside', textinfo='label+value')
+
+# Adding pattern fills
+fig.update_traces(marker=dict(pattern=dict(shape=[".", "/", "+", "-","+"])))
+
+# Showing plot
+fig.show()
+```
+![Pattern Fills Pie Chart](images/plotly-pie-patterns.png)